SoroushVahidi/scidocs huggingface dataset:
An industrial study on the risk of software changes
(2012)

Modelling and understanding bugs has been the focus of much of the Software Engineering research today. However, organizations are interested in more than just bugs. In particular, they are more concerned about managing risk, i.e., the likelihood that a code or design change will cause a negative impact on their products and processes, regardless of whether or not it introduces a bug. In this paper, we conduct a year-long study involving more than 450 developers of a large enterprise, spanning more than 60 teams, to better understand risky changes, i.e., changes for which developers believe that additional attention is needed in the form of careful code or design reviewing and/or more testing. Our findings show that different developers and different teams have their own criteria for determining risky changes. Using factors extracted from the changes and the history of the files modified by the changes, we are able to accurately identify risky changes with a recall of more than 67%, and a precision improvement of 87% (using developer specific models) and 37% (using team specific models), over a random model. We find that the number of lines and chunks of code added by the change, the bugginess of the files being changed, the number of bug reports linked to a change and the developer experience are the best indicators of change risk. In addition, we find that when a change has many related changes, the reliability of developers in marking risky changes is negatively affected. Our findings and models are being used today in practice to manage the risk of software projects.
Generative Modeling with Conditional Autoencoders: Building an Integrated Cell
(2017)

We present a conditional generative model to learn variation in cell and nuclear morphology and the location of subcellular structures from microscopy images. Our model generalizes to a wide range of subcellular localization and allows for a probabilistic interpretation of cell and nuclear morphology and structure localization from fluorescence images. We demonstrate the effectiveness of our approach by producing photorealistic cell images using our generative model. The conditional nature of the model provides the ability to predict the localization of unobserved structures given cell and nuclear morphology.
Stock Direction Forecasting Techniques: An Empirical Study Combining Machine Learning System with Market Indicators in the Indian Context
(2014)

Stock price movement prediction has been one of the most challenging issues in finance since the time immemorial. Many researchers in past have carried out extensive studies with the intention of investigating the approaches that uncover the hidden information in stock market data. As a result of which, Artificial Intelligence and data mining techniques have come to the forefront because of their ability to map nonlinear data. The study encapsulates market indicators with AI techniques to generate useful extracts to improve decisions under conditions of uncertainty. Three approaches (fundamental model, technical indicators model and hybrid model) have been tested using the standalone and integrated machine learning algorithms viz. SVM, ANN, GA-SVM, and GA-ANN and the results of all the three approaches have been compared in the four above mentioned methods. The core objective of this paper is to identify an approach from the above mentioned algorithms that best predicts the Indian stocks price movement. It is observed from the results that the use of GA significantly increases the accuracy of ANN and that the use of technical analysis with SVM and ANN is well suited for Indian stocks and can help investors and traders maximize their quarterly profits.
Computation of the characteristics of a claw pole alternator through the finite element method
(2013)

The paper presents the analysis of a 3-D numerical model developed for a claw pole alternator. The complex structure of the claw-pole magnetic circuit required a 3D FEM model and a double scalar potential magnetic φ-φred formulation, in order to reduce computing time and memory. The no-load and magnetization characteristics and the e.m.f. time dependence have been calculated. Working characteristics and the induced voltage in the static winding can be calculated by knowing the 3D distribution of the field in stationary magnetic regime for successive positions of the rotor considering the stator.
Deep Reinforcement Learning for Optimal Control of Space Heating
(2018)

Classical methods to control heating systems are often marred by suboptimal performance, inability to adapt to dynamic conditions and unreasonable assumptions e.g. existence of building models. This paper presents a novel deep reinforcement learning algorithm which can control space heating in buildings in a computationally efficient manner, and benchmarks it against other known techniques. The proposed algorithm outperforms rule based control by between 5-10% in a simulation environment for a number of price signals. We conclude that, while not optimal, the proposed algorithm offers additional practical advantages such as faster computation times and increased robustness to non-stationarities in building dynamics.
Star Ratings versus Sentiment Analysis -- A Comparison of Explicit and Implicit Measures of Opinions
(2014)

A typical trade-off in decision making is between the cost of acquiring information and the decline in decision quality caused by insufficient information. Consumers regularly face this trade-off in purchase decisions. Online product/service reviews serve as sources of product/service related information. Meanwhile, modern technology has led to an abundance of such content, which makes it prohibitively costly (if possible at all) to exhaust all available information. Consumers need to decide what subset of available information to use. Star ratings are excellent cues for this decision as they provide a quick indication of the tone of a review. However there are cases where such ratings are not available or detailed enough. Sentiment analysis -text analytic techniques that automatically detect the polarity of text- can help in these situations with more refined analysis. In this study, we compare sentiment analysis results with star ratings in three different domains to explore the promise of this technique.
Design and Field Test of a WSN Platform Prototype for Long-Term Environmental Monitoring
(2015)

Long-term wildfire monitoring using distributed in situ temperature sensors is an accurate, yet demanding environmental monitoring application, which requires long-life, low-maintenance, low-cost sensors and a simple, fast, error-proof deployment procedure. We present in this paper the most important design considerations and optimizations of all elements of a low-cost WSN platform prototype for long-term, low-maintenance pervasive wildfire monitoring, its preparation for a nearly three-month field test, the analysis of the causes of failure during the test and the lessons learned for platform improvement. The main components of the total cost of the platform (nodes, deployment and maintenance) are carefully analyzed and optimized for this application. The gateways are designed to operate with resources that are generally used for sensor nodes, while the requirements and cost of the sensor nodes are significantly lower. We define and test in simulation and in the field experiment a simple, but effective communication protocol for this application. It helps to lower the cost of the nodes and field deployment procedure, while extending the theoretical lifetime of the sensor nodes to over 16 years on a single 1 Ah lithium battery.
Building Natural Language Interfaces to Web APIs
(2017)

As the Web evolves towards a service-oriented architecture, application program interfaces (APIs) are becoming an increasingly important way to provide access to data, services, and devices. We study the problem of natural language interface to APIs (NL2APIs), with a focus on web APIs for web services. Such NL2APIs have many potential benefits, for example, facilitating the integration of web services into virtual assistants.
 We propose the first end-to-end framework to build an NL2API for a given web API. A key challenge is to collect training data, i.e., NL command-API call pairs, from which an NL2API can learn the semantic mapping from ambiguous, informal NL commands to formal API calls. We propose a novel approach to collect training data for NL2API via crowdsourcing, where crowd workers are employed to generate diversified NL commands. We optimize the crowdsourcing process to further reduce the cost. More specifically, we propose a novel hierarchical probabilistic model for the crowdsourcing process, which guides us to allocate budget to those API calls that have a high value for training NL2APIs. We apply our framework to real-world APIs, and show that it can collect high-quality training data at a low cost, and build NL2APIs with good performance from scratch. We also show that our modeling of the crowdsourcing process can improve its effectiveness, such that the training data collected via our approach leads to better performance of NL2APIs than a strong baseline.
Contrast-Enhanced Black and White Images
(2015)

This paper investigates contrast enhancement as an approach to tone reduction, aiming to convert a photograph to black and white. Using a filter-based approach to strengthen contrast, we avoid making a hard decision about how to assign tones to segmented regions. Our method is inspired by sticks filtering, used to enhance medical images but not previously used in non-photorealistic rendering. We amplify contrast of pixels along the direction of greatest local difference from the mean, strengthening even weak features if they are most prominent. A final thresholding step converts the contrast-enhanced image to black and white. Local smoothing and contrast enhancement balances abstraction and structure preservation; the main advantage of our method is its faithful depiction of image detail. Our method can create a set of effects: line drawing, hatching, and black and white, all having superior details to previous black and white methods.
The City Browser : Utilizing Massive Call Data to Infer City Mobility Dynamics
(2014)

This paper presents the City Browser, a tool developed to analyze the complexities underlying human mobility at the city scale. The tool uses data generated from mobile phones as a proxy to provide several insights with regards to the commuting patterns of the population within the bounds of a city. The three major components of the browser are the data warehouse, modules and algorithm, and the visualization interface. The modules and algorithm component utilizes Call Detail Records (CDRs) stored within the data warehouse to infer mobility patterns that are then communicated through the visualization interface. The modules and algorithm component consists of four modules: the spatial-temporal decomposition module, the home/work capturing module, the community detection module, and the flow estimation module. The visualization interface manages the output of each module to provide a comprehensive view of a city’s mobility dynamics over varying time scales. A case study is presented on the city of Riyadh in Saudi Arabia, where the browser was developed to better understand city mobility patterns.
A comprehensive framework for testing graphical user interfaces
(2001)

The widespread recognition of the usefulness of graphical user interfaces GUIs has established their importance as critical components of today's software. Although the use of GUIs continues to grow, GUI testing has remained a neglected research area. Since GUIs have c haracteristics that are diierent from those of conventional software, such as user events for input and graphical output, techniques developed to test conventional software cannot bedirectly applied to test GUIs. This thesis develops a uniied solution to the GUI testing problem with the particular goals of automation and integration of tools and techniques used in various phases of GUI testing. These goals are accomplished by developing a GUI testing framework with a GUI model as its central component. For eeciency and scalability, a GUI is represented as a hierarchy of components, each used as a basic unit of testing. The framework also includes a test coverage evaluator, test case generator, test oracle, test executor, and regression tester. The test coverage evaluator employs hierarchical, event-based coverage criteria to automatically specify what to test in a GUI and to determine whether the test suite has adequately tested the GUI. The test case generator employs plan generation techniques from artiicial intelligence to automatically generate a test suite. A test executor automatically executes all the test cases on the GUI. As test cases are being executed, a test oracle automatically determines the correctness of the GUI. The test oracle employs a model of the expected state of the GUI in terms of its constituent objects and their properties. After changes are made to a GUI, a regression tester partitions the original GUI test suite into valid test cases that represent correct inputtoutput for the modiied GUI and invalid test cases that no longer represent correct inputtoutput. The regression tester employs a new technique to reuse some of the invalid test cases by repairing them. A cursory exploration of extending the framework to handle the new testing requirements of web-user interfaces WUIs is also done. The framework iv has been implemented and experiments have demonstrated that the developed techniques are both practical and useful. v Acknowledgements I w ould like to thank my parents whose constant eeorts, encouragement and hard work made achieving the goal of obtaining a Ph.D. possible. I thank all my teachers in schools, colleges, and universities whose dedication and hard work helped lay the foundation for this work. Special thanks …
A Safe, Efficient Regression Test Selection Technique
(1997)

Regression testing is an expensive but necessary maintenance activity performed on modified software to provide confidence that changes are correct and do not adversely affect other portions of the softwore. A regression test selection technique choses, from an existing test set, thests that are deemed necessary to validate modified software. We present a new technique for regression test selection. Our algorithms construct control flow graphs for a precedure or program and its modified version and use these graphs to select tests that execute changed code from the original test suite. We prove that, under certain conditions, the set of tests our technique selects includes every test from the original test suite that con expose faults in the modified procedfdure or program. Under these conditions our algorithms are safe. Moreover, although our algorithms may select some tests that cannot expose faults, they are at lease as precise as other safe regression test selection algorithms. Unlike many other regression test selection algorithms, our algorithms handle all language constructs and all types of program modifications. We have implemented our algorithms; initial empirical studies indicate that our technique can significantly reduce the cost of regression testing modified software.
Coverage criteria for GUI testing
(2001)

A widespread recognition of the usefulness of graphical user interfaces (GUIs) has established their importance as critical components of today's software. GUIs have characteristics different from traditional software, and conventional testing techniques do not directly apply to GUIs. This paper's focus is on coverage critieria for GUIs, important rules that provide an objective measure of test quality. We present new coverage criteria to help determine whether a GUI has been adequately tested. These coverage criteria use events and event sequences to specify a measure of test adequacy. Since the total number of permutations of event sequences in any non-trivial GUI is extremely large, the GUI's hierarchical structure is exploited to identify the important event sequences to be tested. A GUI is decomposed into GUI components, each of which is used as a basic unit of testing. A representation of a GUI component, called an event-flow graph, identifies the interaction of events within a component and intra-component criteria are used to evaluate the adequacy of tests on these events. The hierarchical relationship among components is represented by an integration tree, and inter-component coverage criteria are used to evaluate the adequacy of test sequences that cross components. Algorithms are given to construct event-flow graphs and an integration tree for a given GUI, and to evaluate the coverage of a given test suite with respect to the new coverage criteria. A case study illustrates the usefulness of the coverage report to guide further testing and an important correlation between event-based coverage of a GUI and statement coverage of its software's underlying code.
A Safe, Efficient Algorithm for Regression Test Selection
(1993)

Regression testing is a necessary but costly maintenance activity aimed at demonstrating that code has not been adversely aaected by changes. A selective approach to regression testing selects tests for a modi-ed program from an existing test suite. We present a new technique for selective regression testing. Our algorithm constructs control dependence graphs for program versions, and uses these graphs to determine which tests from the existing test suite may exhibit changed behavior on the new version. Unlike most previous techniques for selective retest, our algorithm selects every test from the original test suite that might expose errors in the modiied program, and does this without prior knowledge of program modiications. Our algorithm handles all language constructs and program modiications, and is easily automated.
An Introduction to Least Commitment Planning
(1994)

Recent developments have clari ed the process of generating partially ordered, partially speci ed sequences of actions whose execution will achive an agent's goal. This paper summarizes a progression of least commitment planners, starting with one that handles the simple strips representation, and ending with one that manages actions with disjunctive precondition, conditional e ects and universal quanti cation over dynamic universes. Along the way we explain how Chapman's formulation of the Modal Truth Criterion is misleading and why his NP-completeness result for reasoning about plans with conditional e ects does not apply to our planner. 1 I thank Franz Amador, Tony Barrett, Darren Cronquist, Denise Draper, Ernie Davis, Oren Etzioni, Nort Fowler, Rao Kambhampati, Craig Knoblock, Nick Kushmerick, Neal Lesh, Karen Lochbaum, Drew McDermott, Ramesh Patil, Kari Pulli, Ying Sun, Austin Tate and Mike Williamson for helpful comments, but retain sole responsibility for errors. This research was funded in part by O ce of Naval Research Grant 90-J-1904 and by National Science Foundation Grant IRI-8957302
Polygenic Influence on Educational Attainment: New evidence from The National Longitudinal Study of Adolescent to Adult Health.
(2015)

Recent studies have begun to uncover the genetic architecture of educational attainment. We build on this work using genome-wide data from siblings in the National Longitudinal Study of Adolescent to Adult Health (Add Health). We measure the genetic predisposition of siblings to educational attainment using polygenic scores. We then test how polygenic scores are related to social environments and educational outcomes. In Add Health, genetic predisposition to educational attainment is patterned across the social environment. Participants with higher polygenic scores were more likely to grow up in socially advantaged families. Even so, the previously published genetic associations appear to be causal. Among pairs of siblings, the sibling with the higher polygenic score typically went on to complete more years of schooling as compared to their lower-scored co-sibling. We found subtle differences between sibling fixed effect estimates of the genetic effect versus those based on unrelated individuals.
A 4-µW 0.8-V rail-to-rail input/output CMOS fully differential OpAmp
(2011)

This paper presents an ultra low power rail-to-rail input/output operational amplifier (OpAmp) designed in a low cost 0.18 µm CMOS technology. In this OpAmp, rail-to-rail input operation is enabled by using complementary input pairs with gm control. To maximize the output swing a rail-to-rail output stage is employed. For low-voltage low-power operation, the operating transistors in the input and output stage are biased in the sub-threshold region. The simulated DC open loop gain is 51 dB, and the slew-rate is 0.04 V/µs with a 10 pF capacitive load connected to each of the amplifier outputs. For the same load, the simulated unity gain frequency is 131 kHz with a 64° phase margin. A common-mode feed-forward circuit (CMFF) increases CMRR, reducing drastically the variations in the output common mode voltage and keeping the DC gain almost constant. In fact, their relative error remains below 1.2 % for a (−20°C, +120°C) temperature span. In addition, the proposed OpAmp is very simple and consumes only 4 µW at 0.8 V supply.
An embarrassingly simple approach to zero-shot learning
(2015)

Zero-shot learning consists in learning how to recognise new concepts by just having a description of them. Many sophisticated approaches have been proposed to address the challenges this problem comprises. In this paper we describe a zero-shot learning approach that can be implemented in just one line of code, yet it is able to outperform state of the art approaches on standard datasets. The approach is based on a more general framework which models the relationships between features, attributes, and classes as a two linear layers network, where the weights of the top layer are not learned but are given by the environment. We further provide a learning bound on the generalisation error of this kind of approaches, by casting them as domain adaptation methods. In experiments carried out on three standard real datasets, we found that our approach is able to perform significantly better than the state of art on all of them, obtaining a ratio of improvement up to 17%.
Mosaic: quantifying privacy leakage in mobile networks
(2013)

With the proliferation of online social networking (OSN) and mobile devices, preserving user privacy has become a great challenge. While prior studies have directly focused on OSN services, we call attention to the privacy leakage in mobile network data. This concern is motivated by two factors. First, the prevalence of OSN usage leaves identifiable digital footprints that can be traced back to users in the real-world. Second, the association between users and their mobile devices makes it easier to associate traffic to its owners. These pose a serious threat to user privacy as they enable an adversary to attribute significant portions of data traffic including the ones with NO identity leaks to network users' true identities. To demonstrate its feasibility, we develop the Tessellation methodology. By applying Tessellation on traffic from a cellular service provider (CSP), we show that up to 50% of the traffic can be attributed to the names of users. In addition to revealing the user identity, the reconstructed profile, dubbed as "mosaic," associates personal information such as political views, browsing habits, and favorite apps to the users. We conclude by discussing approaches for preventing and mitigating the alarming leakage of sensitive user information.